Product Manager, Product & Merchandise Analytics

Vuori•Carlsbad, CA

About The Position

Vuori is seeking a Product Manager to join its Product & Merchandising Analytics pod. The role supports Product Development, Merchandising, Merchandise Planning, Wholesale, and Supply Chain, in partnership with ERP and business systems teams. Working with the Director, the Product Manager shapes the analytics roadmap and independently leads initiatives from discovery through delivery and adoption. The work supports decisions across the product lifecycle, including product cost and margin, assortment strategy, planning, distribution, and inventory management. The Product Manager works with business leaders, analytics leads, engineers, and reporting developers to identify valuable problems and deliver data and tools that address them. The role requires translating open-ended business questions into clearly scoped solutions, managing competing priorities, and communicating effectively with both senior leaders and delivery teams. The position owns initiatives and the product backlog, leads discovery and prioritization, defines requirements and success measures, and drives delivery with the scrum team. It also establishes reliable data and consistent metric definitions, coordinates validation and rollout, monitors adoption and outcomes after launch, manages cross-team dependencies, and explores AI-enabled insights and workflow automation where they add value.

Requirements

At least 5 years of experience in product management, analytics, BI, or a senior analyst role.
Exceptional stakeholder management skills, including leading discussions with senior leaders, gathering requirements across functions, and aligning competing needs into tangible outcomes.
Very strong written and verbal communication skills, with the ability to translate between business and technical audiences.
Strong understanding of retail and product lifecycle analytics, including assortment, seasonality, product cost, margin, sell-through, inventory productivity, and demand and supply planning.
Hands-on experience with a data semantic layer or modern BI platform, such as MicroStrategy, Looker, Power BI semantic models, dbt Semantic Layer, Cube, or equivalent.
Ability to translate business processes into data and analytics requirements, including metric design and definition, data grain, hierarchies, relationships, data quality, edge cases, and versioning.
Experience building analytics products for leadership and operational users, and understanding how to drive adoption and turn data into decisions.
Awareness of AI applications in analytics, including automated insights, semantic search, natural language to SQL, and workflow automation; willingness to prototype, experiment, and use AI tools in personal workflows.

Nice To Haves

Experience supporting product, merchandising, planning, wholesale, or supply chain functions is ideal.
DTC, apparel, retail, or branded consumer goods experience is strongly preferred.

Responsibilities

Partner with the Director to shape priorities for Product & Merchandising Analytics and independently manage initiatives, stakeholder relationships, and day-to-day product decisions.
Lead discovery to understand business decisions and friction, challenge assumptions, and determine whether needs call for reporting, improved data, automation, or workflow changes.
Prioritize requests based on business impact, urgency, effort, and dependencies; recommend what to pursue, defer, or stop and align stakeholders when needs compete.
Translate business questions and operational workflows into requirements, metric definitions, data needs, acceptance criteria, scope, and success measures.
Own the product backlog and partner with engineers and reporting developers on sprint planning, scope decisions, and issue resolution.
Partner with business owners and engineering to connect product, assortment, planning, sales, inventory, purchase order, and ERP data; resolve conflicting definitions, validate results, and communicate data limitations.
Coordinate business testing, resolve issues before release, and ensure teams understand how to use solutions in their workflows.
Assess post-launch impact using adoption, business feedback, and agreed success measures; guide improvements and retire reporting that is no longer needed.
Coordinate shared metrics, data definitions, and delivery dependencies with other analytics pods and business systems teams to avoid conflicting solutions and duplicate work.
Identify opportunities for AI-enabled insights and workflow automation; prototype and validate ideas with Engineering and DS&AI as appropriate, and use approved AI tools in the role’s own work.
Communicate priorities, progress, tradeoffs, and risks; bring recommendations, surface decisions requiring leadership input, and escalate issues before they become delivery blockers.